{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting monthly rainfall in California using Deep Learning Time Series techniques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents <a class=\"anchor\" id=\"0\"></a>\n",
    "* [Introduction](#1) \n",
    "* [Imports](#2)\n",
    "* [Connecting to ArcGIS](#3)\n",
    "* [Accessing & Visualizing the datasets](#4) \n",
    "* [Timeseries Data Preparation](#5) \n",
    "    * [Sequencing Timeseries data](#7)\n",
    "    * [Datatypes of timeseries variable](#8)\n",
    "    * [Checking autocorrelation of timeseries variable](#10)   \n",
    "    * [Train - Test split of timeseries dataset](#6)       \n",
    "* [Timeseries Model Building](#9)\n",
    "    * [Data Preprocessing](#11)  \n",
    "    * [Model Initialization ](#12)\n",
    "    * [Learning Rate Search ](#13)\n",
    "    * [Model Training ](#14) \n",
    "* [Rainfall Forecast & Validation](#15)   \n",
    "    * [Forecasting Using the trained Timeseries Model](#26)\n",
    "    * [Estimate model metrics for validation](#27)\n",
    "    * [Result Visualization](#28)\n",
    "* [Conclusion](#23)\n",
    "* [Summary of methods used](#24)\n",
    "* [Data resources](#25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction <a class=\"anchor\" id=\"1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Forest fires of historical proportions are ravaging various parts of California, started by a long and continuous period of drought. To help in dealing with this growing environmental emergency, utilizing prior knowledge of rainfall is critical. In this sample study, the deepelarning timeseries model from ArcGIS learn will be used to predict monthly rainfall for a whole year at a certain location in the Sierra Nevada foothills, around 30 miles north east of Fresno California, using the location's historic rainfall data. Data from January to December of 2019 will be used to validate the quality of the forecast.\n",
    "\n",
    "Weather forecasting is a popular field for the application of timeseries modelling. There are various approaches used for solving timeseries problems, including classical statistical methods, such as ARIMA group of models, machine learning models, and deep learning models. The current implementation of the ArcGIS learn timeseries model uses state of the art convolutional neural networks backbones especially curated for timeseries datasets. These include InceptionTime, ResCNN, Resnet, and FCN. What makes timeseries modeling unique is that, in the classical methodology of ARIMA, multiple hyperparameters require fine tuning before fitting the model, while with the current deep learning technique, most of the parameters are learned by the model itself from the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports <a class=\"anchor\" id=\"2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "from datetime import datetime as dt\n",
    "from IPython.display import Image, HTML\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from arcgis.gis import GIS\n",
    "from arcgis.learn import TimeSeriesModel, prepare_tabulardata\n",
    "from arcgis.features import FeatureLayer, FeatureLayerCollection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connecting to ArcGIS <a class=\"anchor\" id=\"3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "gis = GIS(\"home\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing & Visualizing datasets  <a class=\"anchor\" id=\"4\"></a> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset used in this sample study is a univariate timeseries dataset of total monthly rainfall from a fixed location of 1 sqkm area in the state of California, ranging from the January 1980 to December 2019."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell downloads the California rainfall dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://services7.arcgis.com/JEwYeAy2cc8qOe3o/arcgis/rest/services/cali_precipitation/FeatureServer/0'\n",
    "table = FeatureLayer(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we preprocess and sort the downloaded dataset by datetime sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>prcp_mm_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980-01-31</td>\n",
       "      <td>224.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1980-02-29</td>\n",
       "      <td>181.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1980-03-31</td>\n",
       "      <td>78.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1980-04-30</td>\n",
       "      <td>34.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1980-05-31</td>\n",
       "      <td>17.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  prcp_mm_\n",
       "1 1980-01-31    224.31\n",
       "4 1980-02-29    181.84\n",
       "5 1980-03-31     78.86\n",
       "8 1980-04-30     34.19\n",
       "9 1980-05-31     17.57"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cali_rainfall_df1 = table.query().sdf\n",
    "cali_rainfall_df1 = cali_rainfall_df1.drop(\"ObjectId\", axis=1)\n",
    "cali_rainfall_df1_sorted = cali_rainfall_df1.sort_values(by='date')\n",
    "cali_rainfall_df1_sorted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 2)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cali_rainfall_df1_sorted.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 480 entries, 1 to 479\n",
      "Data columns (total 2 columns):\n",
      " #   Column    Non-Null Count  Dtype         \n",
      "---  ------    --------------  -----         \n",
      " 0   date      480 non-null    datetime64[ns]\n",
      " 1   prcp_mm_  480 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(1)\n",
      "memory usage: 11.2 KB\n"
     ]
    }
   ],
   "source": [
    "cali_rainfall_df1_sorted.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Timeseries Data Preparation<a class=\"anchor\" id=\"5\"></a>   \n",
    "Preparing the data for timeseries modeling consists of the following three steps:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Sequencing Timeseries data<a class=\"anchor\" id=\"7\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step consist of establishing the sequence of the timeseries data, which is done by creating a new index that is used by the model for processing the sequential data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>prcp_mm_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980-01-31</td>\n",
       "      <td>224.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980-02-29</td>\n",
       "      <td>181.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980-03-31</td>\n",
       "      <td>78.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980-04-30</td>\n",
       "      <td>34.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1980-05-31</td>\n",
       "      <td>17.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  prcp_mm_\n",
       "0 1980-01-31    224.31\n",
       "1 1980-02-29    181.84\n",
       "2 1980-03-31     78.86\n",
       "3 1980-04-30     34.19\n",
       "4 1980-05-31     17.57"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The first step consist of reindexing the timeseries data into a sequential series  \n",
    "cali_rainfall_reindexed = cali_rainfall_df1_sorted.reset_index()\n",
    "cali_rainfall_reindexed = cali_rainfall_reindexed.drop(\"index\", axis=1)\n",
    "cali_rainfall_reindexed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Datatypes of timeseries variable<a class=\"anchor\" id=\"8\"></a> \n",
    "The next step validates the data types of the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 480 entries, 0 to 479\n",
      "Data columns (total 2 columns):\n",
      " #   Column    Non-Null Count  Dtype         \n",
      "---  ------    --------------  -----         \n",
      " 0   date      480 non-null    datetime64[ns]\n",
      " 1   prcp_mm_  480 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(1)\n",
      "memory usage: 7.6 KB\n"
     ]
    }
   ],
   "source": [
    "# check the data types of the variables\n",
    "cali_rainfall_reindexed.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, the timeseries variable is a float, which should be the expected data type. If the variable is not of a float data type, then it needs to be changed to a float data type, as demonstrated by the commented out line in the next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cali_rainfall_reindexed['prcp_mm_'].astype('float64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Checking autocorrelation of timeseries variable<a class=\"anchor\" id=\"10\"></a>\n",
    "The most important step in this process is to determine if the timeseries sequence is autocorrelated. To ensure that our timeseries data can be modeled well, the strength of correlation of the variable with its past data must be estimated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import autocorrelation_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "autocorrelation_plot(cali_rainfall_reindexed[\"prcp_mm_\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows us that there is significant correlation of the data with its immediate time lagged terms, and that it gradually decreases over time as the lag increases. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train - Test split of timeseries dataset<a class=\"anchor\" id=\"6\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset above has 480 data samples each representing monthly ranifall of california for 40 years(1980-2019). Out of this 39 years(1980-2018) of data will be used for training the model and the rest 1 year or a total of 12 months of data are held out for validation. Accordingly the dataset is now split into train and test in the following.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting timeseries data retaining the original sequence by keeping shuffle=False, and test size of 12 months for validation \n",
    "test_size = 12\n",
    "train, test = train_test_split(cali_rainfall_reindexed, test_size = test_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>prcp_mm_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980-01-31</td>\n",
       "      <td>224.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980-02-29</td>\n",
       "      <td>181.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980-03-31</td>\n",
       "      <td>78.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980-04-30</td>\n",
       "      <td>34.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1980-05-31</td>\n",
       "      <td>17.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>2018-09-30</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>13.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>113.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>37.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>468 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  prcp_mm_\n",
       "0   1980-01-31    224.31\n",
       "1   1980-02-29    181.84\n",
       "2   1980-03-31     78.86\n",
       "3   1980-04-30     34.19\n",
       "4   1980-05-31     17.57\n",
       "..         ...       ...\n",
       "463 2018-08-31      0.00\n",
       "464 2018-09-30      0.00\n",
       "465 2018-10-31     13.53\n",
       "466 2018-11-30    113.04\n",
       "467 2018-12-31     37.26\n",
       "\n",
       "[468 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building <a class=\"anchor\" id=\"9\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the dataset is divided into the training and test dataset, the training data is ready to be used for modeling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing <a class=\"anchor\" id=\"11\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, the data used is a univariate timeseries of total monthly rainfall in millimeters. This single variable will be used to forecast the 12 months of rainfall for the months subsequent to the last date in the training data, or put simply, a single variable will be used to predict the future values of that same variable. In the case of a multivariate timeseries model, there would be a list of multiple explanatory variables.\n",
    "\n",
    "Once the variables are identified, the preprocessing of the data is performed by the `prepare_tabulardata` method from the `arcgis.learn` module in the ArcGIS API for Python. This function will take either a non spatial dataframe, a feature layer, or a spatial dataframe containing the dataset as input, and will return a TabularDataObject that can be fed into the model. \n",
    "\n",
    "The primary input parameters required for the tool are:\n",
    "\n",
    "- <span style='background :lightgrey' >input_features</span> : non spatial dataframe, feature layer, or spatial dataframe containing the primary dataset and the explanatory variables, if there are any\n",
    "- <span style='background :lightgrey' >variable_predict</span> : field name containing the y-variable to be forecasted from the input feature layer/dataframe\n",
    "- <span style='background :lightgrey' >explanatory_variables</span> : list of the field names as 2-sized tuples containing the explanatory variables as mentioned above. Since there are none in this example, it is not required here \n",
    "- <span style='background :lightgrey' >index_field</span> : field name containing the timestamp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, preprocessors could be used for scaling the data using a scaler as follows, depending on the data distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preprocessors = [('prcp_mm_', MinMaxScaler())]\n",
    "#data = prepare_tabulardata(train, variable_predict='prcp_mm_', index_field='date', preprocessors=preprocessors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, preprocessors are not used, as the data is normalized by default.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sup10432\\AppData\\Local\\ESRI\\conda\\envs\\pro_dl_28October\\lib\\site-packages\\arcgis\\learn\\_utils\\tabular_data.py:876: UserWarning: Dataframe is not spatial, Rasters and distance layers will not work\n",
      "  warnings.warn(\"Dataframe is not spatial, Rasters and distance layers will not work\")\n"
     ]
    }
   ],
   "source": [
    "data = prepare_tabulardata(train, variable_predict='prcp_mm_', index_field='date', seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the entire timeseries data\n",
    "data.show_batch(graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here sequence length is used as 12 which also indicates the seasonality of the data\n",
    "seq_len=12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the timeseries in batches, here the sequence length is mentioned which would be treated as the batch length\n",
    "data.show_batch(rows=4,seq_len=seq_len)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Initialization <a class=\"anchor\" id=\"12\"></a>\n",
    "\n",
    "This is the most significant step for fitting a timeseries model. Here, along with the data, the backbone for training the model and the sequence length are passed as parameters. Out of these three, the sequence length has to be selected carefully, as it can make or break the model. The sequence length is usually the cycle of the data, which in this case is 12, as it is monthly data and the pattern repeats after 12 months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In model initialization, the data and the backbone is selected from the available set of InceptionTime, ResCNN, Resnet, FCN\n",
    "ts_model = TimeSeriesModel(data, seq_len=seq_len, model_arch='InceptionTime')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning Rate Search<a class=\"anchor\" id=\"13\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finding the learning rate for training the model\n",
    "l_rate = ts_model.lr_find()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Training <a class=\"anchor\" id=\"14\"></a>\n",
    "\n",
    "Finally, the model is now ready for training. To train the model, the `model.fit` method is called and is provided the number of epochs for training and the estimated learning rate suggested by `lr_find` in the previous step:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.083008</td>\n",
       "      <td>0.037609</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.061329</td>\n",
       "      <td>0.036617</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.050808</td>\n",
       "      <td>0.036499</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.044877</td>\n",
       "      <td>0.036835</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.039619</td>\n",
       "      <td>0.036479</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.034815</td>\n",
       "      <td>0.034671</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.031023</td>\n",
       "      <td>0.032002</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.028147</td>\n",
       "      <td>0.030694</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.025152</td>\n",
       "      <td>0.026627</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.022742</td>\n",
       "      <td>0.024633</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.020327</td>\n",
       "      <td>0.026560</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.018206</td>\n",
       "      <td>0.023352</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.016312</td>\n",
       "      <td>0.024652</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.014641</td>\n",
       "      <td>0.026808</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.013142</td>\n",
       "      <td>0.026944</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.011842</td>\n",
       "      <td>0.024464</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.010730</td>\n",
       "      <td>0.024647</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.009880</td>\n",
       "      <td>0.023319</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.009031</td>\n",
       "      <td>0.025791</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.008297</td>\n",
       "      <td>0.024947</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.007618</td>\n",
       "      <td>0.026807</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.007105</td>\n",
       "      <td>0.025442</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.006866</td>\n",
       "      <td>0.026500</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.006573</td>\n",
       "      <td>0.028415</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.006338</td>\n",
       "      <td>0.023184</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.006256</td>\n",
       "      <td>0.030714</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.006376</td>\n",
       "      <td>0.028586</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.006548</td>\n",
       "      <td>0.026342</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.006665</td>\n",
       "      <td>0.020448</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.006532</td>\n",
       "      <td>0.029441</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.006306</td>\n",
       "      <td>0.025255</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.006137</td>\n",
       "      <td>0.025174</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.005978</td>\n",
       "      <td>0.023557</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.005687</td>\n",
       "      <td>0.023234</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.005332</td>\n",
       "      <td>0.025562</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.004955</td>\n",
       "      <td>0.023331</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.004672</td>\n",
       "      <td>0.023735</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.004330</td>\n",
       "      <td>0.023847</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.004095</td>\n",
       "      <td>0.026608</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.003849</td>\n",
       "      <td>0.022369</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.003577</td>\n",
       "      <td>0.023238</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.003376</td>\n",
       "      <td>0.023750</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.003158</td>\n",
       "      <td>0.023843</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.002963</td>\n",
       "      <td>0.023013</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.002801</td>\n",
       "      <td>0.022028</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.002652</td>\n",
       "      <td>0.023180</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.002558</td>\n",
       "      <td>0.024409</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.002417</td>\n",
       "      <td>0.022479</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.002264</td>\n",
       "      <td>0.023945</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.002172</td>\n",
       "      <td>0.022920</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.002085</td>\n",
       "      <td>0.021997</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.001990</td>\n",
       "      <td>0.023793</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>0.024170</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.001774</td>\n",
       "      <td>0.023699</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.001724</td>\n",
       "      <td>0.023237</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.001611</td>\n",
       "      <td>0.023164</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.001527</td>\n",
       "      <td>0.022908</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.001451</td>\n",
       "      <td>0.023040</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.001390</td>\n",
       "      <td>0.022155</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.001347</td>\n",
       "      <td>0.022041</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.001333</td>\n",
       "      <td>0.022265</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.001291</td>\n",
       "      <td>0.022561</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.001224</td>\n",
       "      <td>0.021779</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.001159</td>\n",
       "      <td>0.022445</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.001080</td>\n",
       "      <td>0.022740</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.001023</td>\n",
       "      <td>0.022691</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.000971</td>\n",
       "      <td>0.023290</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.000941</td>\n",
       "      <td>0.022854</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.000876</td>\n",
       "      <td>0.022432</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.000829</td>\n",
       "      <td>0.022994</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.000774</td>\n",
       "      <td>0.022587</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.000722</td>\n",
       "      <td>0.022578</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.000681</td>\n",
       "      <td>0.022348</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.000643</td>\n",
       "      <td>0.022647</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.000608</td>\n",
       "      <td>0.022395</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.000569</td>\n",
       "      <td>0.022518</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.000533</td>\n",
       "      <td>0.022561</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.000510</td>\n",
       "      <td>0.022146</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.000477</td>\n",
       "      <td>0.022354</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.022084</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>0.022166</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.022053</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>0.022132</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.000370</td>\n",
       "      <td>0.022445</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.022072</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.000344</td>\n",
       "      <td>0.022071</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.000322</td>\n",
       "      <td>0.022196</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.000305</td>\n",
       "      <td>0.022326</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.022460</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>0.022545</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.022607</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.022609</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.022556</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.000225</td>\n",
       "      <td>0.022444</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.022330</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.000215</td>\n",
       "      <td>0.022326</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.000212</td>\n",
       "      <td>0.022314</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.022303</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.000226</td>\n",
       "      <td>0.022315</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.022314</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts_model.fit(100, lr=l_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the train vs valid losses is plotted to check quality of the trained model, and whether the model needs more training \n",
    "ts_model.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the predicted values by the trained model is printed for the test set\n",
    "ts_model.show_results(rows=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figures above display the training and the validation of the prediction attained by the model while training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rainfall Forecast & Validation <a class=\"anchor\" id=\"15\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forecasting Using the trained Timeseries Model <a class=\"anchor\" id=\"26\"></a>\n",
    "During forecasting, the model will use the same training dataset as input and will use the last sequence length number of terms from that dataset's tail to predict the rainfall for the number of months specified by the user.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>prcp_mm_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980-01-31</td>\n",
       "      <td>224.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980-02-29</td>\n",
       "      <td>181.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980-03-31</td>\n",
       "      <td>78.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980-04-30</td>\n",
       "      <td>34.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1980-05-31</td>\n",
       "      <td>17.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>2018-09-30</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>13.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>113.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>37.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>468 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  prcp_mm_\n",
       "0   1980-01-31    224.31\n",
       "1   1980-02-29    181.84\n",
       "2   1980-03-31     78.86\n",
       "3   1980-04-30     34.19\n",
       "4   1980-05-31     17.57\n",
       "..         ...       ...\n",
       "463 2018-08-31      0.00\n",
       "464 2018-09-30      0.00\n",
       "465 2018-10-31     13.53\n",
       "466 2018-11-30    113.04\n",
       "467 2018-12-31     37.26\n",
       "\n",
       "[468 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the training dataset\n",
    "train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Forecasting requires the format of the date column to be in datetime. If the date column is not in the datetime format, it can be changed to datetime by using the `pd.to_datetime()` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 468 entries, 0 to 467\n",
      "Data columns (total 2 columns):\n",
      " #   Column    Non-Null Count  Dtype         \n",
      "---  ------    --------------  -----         \n",
      " 0   date      468 non-null    datetime64[ns]\n",
      " 1   prcp_mm_  468 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(1)\n",
      "memory usage: 11.0 KB\n"
     ]
    }
   ],
   "source": [
    "# checking if the datatype of the 'date' column is in datetime format\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, the date column is already in the required datetime format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>prcp_mm_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>2018-09-30</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>13.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>113.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>37.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  prcp_mm_\n",
       "463 2018-08-31      0.00\n",
       "464 2018-09-30      0.00\n",
       "465 2018-10-31     13.53\n",
       "466 2018-11-30    113.04\n",
       "467 2018-12-31     37.26"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally the predict function is used to forecast for a period of the 12 months subsequent to the last date in the training dataset. As such, this will be forecasting rainfall for the 12 months of 2019, starting from January of 2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sup10432\\AppData\\Local\\ESRI\\conda\\envs\\pro_dl_28October\\lib\\site-packages\\pandas\\core\\indexing.py:670: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  iloc._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "# Here the forecast is returned as a dataframe, since it is non spatial data, mentioned in the 'prediction_type'     \n",
    "sdf_forecasted = ts_model.predict(train, prediction_type='dataframe', number_of_predictions=test_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>prcp_mm_</th>\n",
       "      <th>prcp_mm__results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980-01-31</td>\n",
       "      <td>224.31</td>\n",
       "      <td>224.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980-02-29</td>\n",
       "      <td>181.84</td>\n",
       "      <td>181.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980-03-31</td>\n",
       "      <td>78.86</td>\n",
       "      <td>78.860000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980-04-30</td>\n",
       "      <td>34.19</td>\n",
       "      <td>34.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1980-05-31</td>\n",
       "      <td>17.57</td>\n",
       "      <td>17.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>2019-08-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.099367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>2019-09-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.751540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>2019-10-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.207242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>2019-11-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83.685172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>2019-12-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>142.722045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  prcp_mm_  prcp_mm__results\n",
       "0   1980-01-31    224.31        224.310000\n",
       "1   1980-02-29    181.84        181.840000\n",
       "2   1980-03-31     78.86         78.860000\n",
       "3   1980-04-30     34.19         34.190000\n",
       "4   1980-05-31     17.57         17.570000\n",
       "..         ...       ...               ...\n",
       "475 2019-08-20       NaN         -5.099367\n",
       "476 2019-09-18       NaN         10.751540\n",
       "477 2019-10-17       NaN         25.207242\n",
       "478 2019-11-15       NaN         83.685172\n",
       "479 2019-12-14       NaN        142.722045\n",
       "\n",
       "[480 rows x 3 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# final forecasted result returned by the model\n",
    "sdf_forecasted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prcp_mm__results</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-29</th>\n",
       "      <td>129.882450</td>\n",
       "      <td>149.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-27</th>\n",
       "      <td>144.863222</td>\n",
       "      <td>222.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-28</th>\n",
       "      <td>143.764554</td>\n",
       "      <td>102.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-26</th>\n",
       "      <td>48.772594</td>\n",
       "      <td>25.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-25</th>\n",
       "      <td>61.366233</td>\n",
       "      <td>102.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            prcp_mm__results  actual\n",
       "date                                \n",
       "2019-01-29        129.882450  149.69\n",
       "2019-02-27        144.863222  222.38\n",
       "2019-03-28        143.764554  102.48\n",
       "2019-04-26         48.772594   25.12\n",
       "2019-05-25         61.366233  102.49"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Formating the result into actual vs the predicted columns\n",
    "sdf_forecasted = sdf_forecasted.tail(test_size)\n",
    "sdf_forecasted = sdf_forecasted[['date','prcp_mm__results']]\n",
    "sdf_forecasted['actual'] = test[test.columns[-1]].values\n",
    "sdf_forecasted = sdf_forecasted.set_index(sdf_forecasted.columns[0])\n",
    "sdf_forecasted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate model metrics for validation <a class=\"anchor\" id=\"27\"></a>\n",
    "The accuracy of the forecasted values is measured by comparing the forecasted values against the actual values of the 12 months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "import sklearn.metrics as metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Square:  0.8\n"
     ]
    }
   ],
   "source": [
    "r2_test = r2_score(sdf_forecasted['actual'],sdf_forecasted['prcp_mm__results'])\n",
    "print('R-Square: ', round(r2_test, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A considerably high r-squared value indicates a high similarity between the forecasted and the actual sales values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:  32.2893\n",
      "MAE:  25.5549\n"
     ]
    }
   ],
   "source": [
    "mse_RF_train = metrics.mean_squared_error(sdf_forecasted['actual'], sdf_forecasted['prcp_mm__results'])\n",
    "print('RMSE: ', round(np.sqrt(mse_RF_train), 4))\n",
    "\n",
    "mean_absolute_error_RF_train = metrics.mean_absolute_error(sdf_forecasted['actual'], sdf_forecasted['prcp_mm__results'])\n",
    "print('MAE: ', round(mean_absolute_error_RF_train, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error terms of RMSE and MAE in the forecasting are 32.28mm and 25.55mm respectively, which are quite low."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result Visualization<a class=\"anchor\" id=\"28\"></a>\n",
    "\n",
    "Finally, the actual and forecasted values are plotted to visualize their distribution over the 12 months period, with the blue lines indicating forecasted values and orange line showing the actual values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "plt.plot(sdf_forecasted)\n",
    "plt.ylabel('prcp_mm__results')\n",
    "plt.legend(sdf_forecasted.columns.values,loc='upper right')\n",
    "plt.title( 'Rainfall Forecast')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion<a class=\"anchor\" id=\"23\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The newly implemented deeplearning timeseries model from the arcgis.learn library was used to forecast monthly rainfall for a location of 1 sqkm in California, for the period of January to December 2019, which it was able to model with a high accuracy. The notebook elaborates on the methodology of applying the model for forecasting time series data. The process includes first preparing a timeseries dataset using the prepare_tabulardata() method, followed by modeling and fitting the dataset. Usually, timeseries modelling requires fine tuning several hyperparameters for properly fitting the data, most of which has been internalized in this current implementation, leaving the user responsible for configuring only a few significant parameters, like the sequence length.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of methods used <a class=\"anchor\" id=\"24\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Method | Description | Examples |\n",
    "| -| - |-|\n",
    "| prepare_tabulardata| prepare data including imputation, normalization and train-test split  |prepare data ready for fitting a  Timeseries Model \n",
    "| model.lr_find()| find an optimal learning rate  | finalize a good learning rate for training the Timeseries model\n",
    "| TimeSeriesModel() | Model Initialization by selecting the Timeseries Deeplearning algorithm to be used for fitting  | Selected Timsereis algorithm from Fastai timeseries regression can be used\n",
    "| model.fit() | train a model with epochs & learning rate as input  | training the Timeseries model with sutiable input \n",
    "| model.score() | find the model metric of R-squared of the trained model  | returns R-squared value after training the Timeseries Model \n",
    "| model.predict() | predict on a test set | forecast values using the trained models on test input "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data resources <a class=\"anchor\" id=\"25\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Dataset | Source | Link |\n",
    "| -| - |-|\n",
    "|California Daily weather data|MODIS - Daily Surface Weather Data on a 1-km Grid for North America, Version 3|https://daac.ornl.gov/DAYMET/guides/Daymet_V3_CFMosaics.html|"
   ]
  }
 ],
 "metadata": {
  "esriNotebookRuntime": {
   "notebookRuntimeName": "ArcGIS Notebook Python 3 Advanced with GPU support",
   "notebookRuntimeVersion": ""
  },
  "kernelspec": {
   "display_name": "pro_zion_11Nov",
   "language": "python",
   "name": "pro_zion_11nov"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
